Pro$: A New Profit-Based Genetic Selection Index in Canada
Bibliographic record
Abstract
Pro$ (pronounced Pro Dollars) was recently developed by Canadian Dairy Network (CDN) as a second national index that targets dairy producers who generate essentially all of their farm revenue from milk sales. Actual cow profitability data provided to producers by dairy herd improvement (DHI) agencies in Canada, namely CanWest DHI and Valacta, was used as the basis for deriving the new profit-based genetic selection index. Economic parameters used to calculate profitability for each cow are updated annually by economists to reflect changes in milk pricing as well as the associated expenses, including overhead, maintenance feed costs, marginal feed costs and quota opportunity costs. Data used was the accumulated profit to 6 years of age for 672,254 registered Holstein cows with known sire identification, born from January 2005 to September 2008. For cows not surviving to 6 years of age, accumulated profit to the date they left the herd was considered as lifetime profit. For each sire, the average accumulated profit of daughters to 6 years of age was computed. A total of 830 sires with at least 100 daughters with profit data were used to conduct the two-step multiple trait regression analysis to determine the contribution of sire EBVs for three production, four major type, and eight functional traits in predicting the average daughter profit to 6 years of age. Adjusted R-squared of the Pro$ prediction equation was .6167, which can be applied to any dairy breed with the appropriate scaling factors. Relative to LPI, selection for Pro$ in Holsteins has an stronger expected response for Milk and Protein Yields as well as various functional traits, including Herd Life, while both indexes have similar selection responses for Fat Yield, Daughter Fertility, Mastitis Resistance and Rump. Effective August 2015, Pro$ will be available in the Holstein and Jersey breeds and will be expressed in dollar terms as a deviation from breed average. For other dairy breeds, the research behind the development of Pro$ was used to modify the LPI formula effective August 2015 to better reflect expected average daughter profit from milk sales.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".